(36g) A Simulation-Based Derivative-Free Optimization Framework Using the Kinetic Monte Carlo Method for Controlling Polymer Molecular Weight and Sequence Distribution Synthesized Via Free Radical Polymerization
AIChE Annual Meeting
Sunday, October 29, 2017 - 5:15pm to 5:30pm
In this work, we developed an optimization framework based on a derivative-free method that incorporates KMC simulations to efficiently find synthesis conditions for property targets, including molecular weight and sequence. The derivative-free optimization algorithm is named Bounded Optimization BY Quadratic Approximation (BOBYQA) developed by Powell5. This algorithm finds the minimum of a differentiable âblack boxâ function calculated from the output of a KMC simulation, which we have adopted to return properties of a polymer for given synthesis conditions.
The performance of our model is demonstrated on a copolymerization system of butyl acrylate (BA) and methyl methacrylate (MMA) in a batch reactor. We show by two case studies that the model can accurately and efficiently control molecular weight and sequence targets by changing reaction conditions such as temperature, monomer composition and overall conversion. The algorithm can also provide insight on designing reactivity ratios to better meet the property targets. In addition, the explicit sequence information generated from KMC allows for detailed analysis and control of the distribution of sequence characteristics among different chains.
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